A quest for the structure of intra- and postoperative surgical team networks: does the small-world property evolve over time?
We examined the structure of intra- and postoperative case-collaboration networks among the surgical service providers in a quaternary-care academic medical center, using retrospective electronic medical record (EMR) data. We also analyzed the evolution of the network properties over time, as changes in nodes and edges can affect the network structure. We used de-identified intra- and postoperative data for adult patients, ages ≥ 21, who received nonambulatory/nonobstetric surgery at Shands at the University of Florida between June 1, 2011 and November 1, 2014. The intraoperative segment contained 30,245 surgical cases, and the postoperative segment considered 30,202 hospitalizations. Our results confirmed the existence of small-world structure in both intra- and postoperative surgical team networks. In addition, high network density was observed in the intraoperative segment and partially in postoperative one, representing the existence of cohesive clusters of providers. We also observed that the small-world property is exhibited more in the intraoperative compared to the postoperative network. Analyzing the temporal aspects of the networks revealed that the postoperative segment tends to lose its cohesiveness as time passes. Finally, we observed the small-world structure is negatively related to patients’ outcome in both intra- and postoperative networks whereas the relation between the outcome and network density is positive. Small changes in graph-theoretic properties of the intra- and postoperative networks cause changes in the intensity of the structural properties. However, due to the special characteristics of the examined networks (e.g., high interconnectivity, team oriented), the network is less likely to lose its structural properties unless the central hubs are removed. Our results highlight the importance of stability of personnel in key positions. This highlights the important role of the central players in the network that offers change leaders the opportunity to quantify and target those nodes as mediators of process change.
KeywordsSurgery Anesthesia Network structure analysis Intra- and postoperative Small world Cohesion
Conceiving and designing the experiments: AE, PJT, PR. Performing the experiments: AE. Analyzing the data: AE. Data/materials: PJT, LZ. Writing of the manuscript: AE, PJT, LZ, PR.
Compliance with ethical standards
Conflict of interest
The authors have no financial and/or non-financial competing interests to declare.
The University of Florida Institutional Review Board (IRB) approved this study (IRB number 201400976). The data for this research were collected from the University of Florida’s Integrated Data Repository (IDR) after obtaining a confidentiality agreement from the IDR.
The Social Network Analysis and Mining (SNAM) journal has authors’ permission to publish the article.
- Burt RS (2009) Structural holes: the social structure of competition. Harvard University Press, CambridgeGoogle Scholar
- Doll KM, Meng K, Gehrig PA, Brewster WR, Meyer A (2016) Referral patterns between high-and low-volume centers and associations with uterine cancer treatment and survival: a population-based study of Medicare, Medicaid, and privately insured women. Am J Obstet Gynecol 215(4):447 e1–447 e13CrossRefGoogle Scholar
- Donoho DL (1982) Breakdown properties of multivariate location estimators. Technical Report, Harvard University, Boston. http://www.Stat.Stanford.Edu/~donoho/Reports/Oldies/BPMLE.Pdf. Accessed Dec 2018
- Ebadi A, Tighe PJ, Zhang L, Rashidi P (2016) On the scale-free characteristics of surgical team networks. In: Paper presented at the 12th international conference on webometrics, infometrics, scientometrics and 17th collnet meeting, FranceGoogle Scholar
- Fitzgerald TL, Seymore NM, Kachare SD, Zervos EE, Wong JH (2013) Measuring the impact of multidisciplinary care on quality for pancreatic surgery: transition to a focused, very high-volume program. Am Surg 79(8):775–780Google Scholar
- Gerard RJ (1995) Teaming up: making the transition to a self-directed, team-based organization. Acad Manag Exec 9(3):91–93Google Scholar
- Glance LG, Dick A, Osler TM, Li Y, Mukamel DB (2006) Impact of changing the statistical methodology on hospital and surgeon ranking: the case of the New York state cardiac surgery report card. Med Care 44(4):311–319. https://doi.org/10.1097/01.mlr.0000204106.64619.2a CrossRefGoogle Scholar
- Hanneman RA, Riddle M (2011) Concepts and measures for basic network analysis. In: Carrington P, Scott J (eds) The SAGE handbook of social network analysis. SAGE Ltd., Thousand Oaks, CA, pp 340–369Google Scholar
- Lawrence DE (2003) Cluster-based bounded influence regression (Doctoral dissertation)Google Scholar
- Lin N (2002) Social capital: a theory of social structure and action. Cambridge University Press, CambridgeGoogle Scholar
- Paige J, Kozmenko V, Morgan B, Howell DS, Chauvin S, Hilton C et al (2007) From the flight deck to the operating room: an initial pilot study of the feasibility and potential impact of true interdisciplinary team training using high-fidelity simulation. J Surg Educ 64(6):369–377CrossRefGoogle Scholar
- Preston L, Turner J, Booth A, O’Keeffe C, Campbell F, Jesurasa A et al (2015) Is there a relationship between surgical case volume and mortality in congenital heart disease services? A rapid evidence review. BMJ Open 5(12):e009252–e002015. https://doi.org/10.1136/bmjopen-2015-009252 CrossRefGoogle Scholar
- Samarth CN, Gloor PA (2009) Process efficiency. redesigning social networks to improve surgery patient flow. J Healthc Inf Manag JHIM 23(1):20–26Google Scholar
- Shirinivas S, Vetrivel S, Elango N (2010) Applications of graph theory in computer science an overview. Int J Eng Sci Technol 2(9):4610–4621Google Scholar
- Stahel WA (1981a) Breakdown of covariance estimators. (No. Research Report 31). Fachgruppe für Statistik, Eidgenössische Techn. HochschGoogle Scholar
- Stahel WA (1981b) Robuste schatzungen: Infinitisimale optimalitat und schatzunguen von kovarianzmatrizen (Ph.d. thesis no. 6881). http://e-collection.ethbib.ethz.ch/view/eth:21890. Accessed Dec 2018
- Tighe PJ, Patel SS, Gravenstein N, Davies L, Lucas SD, Bernard HR (2014) The operating room: It’sa small world (and scale free network) after all. J Insna 34(1&2)Google Scholar
- Weller J, Civil I, Torrie J, Cumin D, Garden A, Corter A, Merry A (2016) Can team training make surgery safer? lessons for national implementation of a simulation-based programme. N Z Med J 129 (1443):9–17Google Scholar